Department of Biotechnology, Graduate School of Engineering, Nagoya University, Nagoya, Aichi, Japan.
Department of Computer Science/Scientific and Engineering Simulation, Graduate School of Engineering, Nagoya Institute of Technology, Nagoya, Aichi, Japan.
PLoS One. 2014 Apr 4;9(4):e93952. doi: 10.1371/journal.pone.0093952. eCollection 2014.
Precise quantification of cellular potential of stem cells, such as human bone marrow-derived mesenchymal stem cells (hBMSCs), is important for achieving stable and effective outcomes in clinical stem cell therapy. Here, we report a method for image-based prediction of the multiple differentiation potentials of hBMSCs. This method has four major advantages: (1) the cells used for potential prediction are fully intact, and therefore directly usable for clinical applications; (2) predictions of potentials are generated before differentiation cultures are initiated; (3) prediction of multiple potentials can be provided simultaneously for each sample; and (4) predictions of potentials yield quantitative values that correlate strongly with the experimental data. Our results show that the collapse of hBMSC differentiation potentials, triggered by in vitro expansion, can be quantitatively predicted far in advance by predicting multiple potentials, multi-lineage differentiation potentials (osteogenic, adipogenic, and chondrogenic) and population doubling potential using morphological features apparent during the first 4 days of expansion culture. In order to understand how such morphological features can be effective for advance predictions, we measured gene-expression profiles of the same early undifferentiated cells. Both senescence-related genes (p16 and p21) and cytoskeleton-related genes (PTK2, CD146, and CD49) already correlated to the decrease of potentials at this stage. To objectively compare the performance of morphology and gene expression for such early prediction, we tested a range of models using various combinations of features. Such comparison of predictive performances revealed that morphological features performed better overall than gene-expression profiles, balancing the predictive accuracy with the effort required for model construction. This benchmark list of various prediction models not only identifies the best morphological feature conversion method for objective potential prediction, but should also allow clinicians to choose the most practical morphology-based prediction method for their own purposes.
精确量化干细胞的潜能，如人骨髓间充质干细胞（hBMSCs），对于实现临床干细胞治疗的稳定和有效结果非常重要。在这里，我们报告了一种基于图像的预测 hBMSCs 多种分化潜能的方法。该方法具有四个主要优点：（1）用于潜能预测的细胞是完整的，因此可直接用于临床应用；（2）在开始分化培养之前生成潜能预测；（3）可以同时为每个样本提供多种潜能的预测；（4）潜能预测产生的定量值与实验数据高度相关。我们的结果表明，体外扩增引发的 hBMSC 分化潜能的崩溃，可以通过预测多个潜能、多谱系分化潜能（成骨、成脂和成软骨）和群体倍增潜能，从形态学特征上进行早期预测。使用第 4 天之前的扩增培养中的形态特征，对 hBMSC 分化潜能进行定量预测。为了了解这些形态特征如何能有效进行早期预测，我们测量了相同早期未分化细胞的基因表达谱。在这个阶段，衰老相关基因（p16 和 p21）和细胞骨架相关基因（PTK2、CD146 和 CD49）已经与潜能的下降相关。为了客观地比较形态学和基因表达在这种早期预测中的性能，我们使用各种特征组合测试了一系列模型。这种预测性能的比较表明，形态特征的总体性能优于基因表达谱，可以在模型构建所需的努力和预测准确性之间取得平衡。这种各种预测模型的基准列表不仅确定了用于客观潜能预测的最佳形态特征转换方法，还应允许临床医生根据自己的目的选择最实用的基于形态学的预测方法。
